{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.155014Z",
     "start_time": "2020-11-17T13:25:27.141578Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from collections import Counter\n",
    "\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 300\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read files and select drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.159148Z",
     "start_time": "2020-11-17T13:25:27.156592Z"
    }
   },
   "outputs": [],
   "source": [
    "ref_type = 'log2_median_ic50_hn' # log2_median_ic50_3f_hn | log2_median_ic50_hn\n",
    "model_name = 'RWEN' \n",
    "\n",
    "# for each patient, if cell cluster is less than 5%, then we don't consider that cluster \n",
    "freq_cutoff = 0.05\n",
    "\n",
    "# shift the dosage as GDSC experiment (Syto60) is less sensitive\n",
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.171741Z",
     "start_time": "2020-11-17T13:25:27.161549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81, 27)\n"
     ]
    }
   ],
   "source": [
    "drug_info_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "drug_info_df.index = drug_info_df.index.astype(str)\n",
    "\n",
    "drug_id_name_dict = dict(zip(drug_info_df.index, drug_info_df['Drug Name']))\n",
    "print (drug_info_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.178800Z",
     "start_time": "2020-11-17T13:25:27.173561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tested_drug_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "[d for d in tested_drug_list if d not in drug_info_df.index.astype(int)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read predicted IC50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.183204Z",
     "start_time": "2020-11-17T13:25:27.180764Z"
    }
   },
   "outputs": [],
   "source": [
    "norm_type = 'TPM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.192017Z",
     "start_time": "2020-11-17T13:25:27.184966Z"
    }
   },
   "outputs": [],
   "source": [
    "cadrres_cluster_df = pd.read_csv('../result/HN_model/{}/{}_pred.csv'.format(norm_type, model_name), index_col=0)\n",
    "out_dir = '../result/HN_model/{}/'.format(norm_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.206364Z",
     "start_time": "2020-11-17T13:25:27.193869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1007</th>\n",
       "      <th>133</th>\n",
       "      <th>201</th>\n",
       "      <th>1010</th>\n",
       "      <th>182</th>\n",
       "      <th>301</th>\n",
       "      <th>302</th>\n",
       "      <th>1012</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>-9.408460</td>\n",
       "      <td>-6.029518</td>\n",
       "      <td>-11.469267</td>\n",
       "      <td>2.835190</td>\n",
       "      <td>-4.326052</td>\n",
       "      <td>-2.212837</td>\n",
       "      <td>-2.816472</td>\n",
       "      <td>-0.354483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>-9.454257</td>\n",
       "      <td>-6.286787</td>\n",
       "      <td>-11.783106</td>\n",
       "      <td>1.652145</td>\n",
       "      <td>-3.675764</td>\n",
       "      <td>-2.277713</td>\n",
       "      <td>-4.730575</td>\n",
       "      <td>-0.613253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B1</th>\n",
       "      <td>-8.610804</td>\n",
       "      <td>-5.822999</td>\n",
       "      <td>-10.011056</td>\n",
       "      <td>3.069076</td>\n",
       "      <td>-0.747959</td>\n",
       "      <td>9.088354</td>\n",
       "      <td>5.539899</td>\n",
       "      <td>-0.177654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>-9.238351</td>\n",
       "      <td>-6.119612</td>\n",
       "      <td>-9.166921</td>\n",
       "      <td>3.319112</td>\n",
       "      <td>-1.294344</td>\n",
       "      <td>2.519170</td>\n",
       "      <td>0.776686</td>\n",
       "      <td>0.146506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>-9.991132</td>\n",
       "      <td>-6.558564</td>\n",
       "      <td>-12.666164</td>\n",
       "      <td>2.232760</td>\n",
       "      <td>-1.774887</td>\n",
       "      <td>4.421201</td>\n",
       "      <td>0.631304</td>\n",
       "      <td>-0.866772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1007       133        201      1010       182       301  \\\n",
       "cluster                                                                \n",
       "A1      -9.408460 -6.029518 -11.469267  2.835190 -4.326052 -2.212837   \n",
       "A2      -9.454257 -6.286787 -11.783106  1.652145 -3.675764 -2.277713   \n",
       "B1      -8.610804 -5.822999 -10.011056  3.069076 -0.747959  9.088354   \n",
       "B2      -9.238351 -6.119612  -9.166921  3.319112 -1.294344  2.519170   \n",
       "C1      -9.991132 -6.558564 -12.666164  2.232760 -1.774887  4.421201   \n",
       "\n",
       "              302      1012  \n",
       "cluster                      \n",
       "A1      -2.816472 -0.354483  \n",
       "A2      -4.730575 -0.613253  \n",
       "B1       5.539899 -0.177654  \n",
       "B2       0.776686  0.146506  \n",
       "C1       0.631304 -0.866772  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cadrres_cluster_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.215763Z",
     "start_time": "2020-11-17T13:25:27.209348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 24\n"
     ]
    }
   ],
   "source": [
    "drug_list = cadrres_cluster_df.columns\n",
    "cluster_list = cadrres_cluster_df.index\n",
    "print(len(drug_list), len(cluster_list))\n",
    "\n",
    "drug_info_df = drug_info_df.loc[drug_list]\n",
    "cadrres_cluster_df = cadrres_cluster_df[drug_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.221199Z",
     "start_time": "2020-11-17T13:25:27.218100Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    # Shift by 4 uM\n",
    "    cadrres_cluster_df = cadrres_cluster_df - 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read cell cluster % in each patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.271347Z",
     "start_time": "2020-11-17T13:25:27.223321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cluster</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>E1</th>\n",
       "      <th>E2</th>\n",
       "      <th>...</th>\n",
       "      <th>F3</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>H1</th>\n",
       "      <th>I1</th>\n",
       "      <th>I2</th>\n",
       "      <th>J1</th>\n",
       "      <th>J2</th>\n",
       "      <th>K1</th>\n",
       "      <th>L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HN120</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313187</td>\n",
       "      <td>0.175824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.120879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.032967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN137</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005682</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.085227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096591</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.011364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN148</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313514</td>\n",
       "      <td>0.205405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.021622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN159</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317365</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48503</td>\n",
       "      <td>0.011976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN160</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>0.414815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.162963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster           A1        A2        B1        B2        C1        C2  \\\n",
       "patient_id                                                               \n",
       "HN120       0.010989  0.005495  0.000000  0.000000  0.000000  0.000000   \n",
       "HN137       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "HN148       0.000000  0.000000  0.000000  0.000000  0.313514  0.205405   \n",
       "HN159       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "HN160       0.000000  0.000000  0.422222  0.414815  0.000000  0.000000   \n",
       "\n",
       "cluster           D1        D2        E1        E2  ...        F3        G1  \\\n",
       "patient_id                                          ...                       \n",
       "HN120       0.313187  0.175824  0.000000  0.000000  ...  0.000000  0.340659   \n",
       "HN137       0.005682  0.000000  0.340909  0.085227  ...  0.096591  0.000000   \n",
       "HN148       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "HN159       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "HN160       0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "\n",
       "cluster           G2        H1        I1        I2   J1   J2       K1  \\\n",
       "patient_id                                                              \n",
       "HN120       0.120879  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN137       0.000000  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN148       0.000000  0.459459  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "HN159       0.000000  0.000000  0.317365  0.185629  0.0  0.0  0.48503   \n",
       "HN160       0.000000  0.000000  0.000000  0.000000  0.0  0.0  0.00000   \n",
       "\n",
       "cluster            L  \n",
       "patient_id            \n",
       "HN120       0.032967  \n",
       "HN137       0.011364  \n",
       "HN148       0.021622  \n",
       "HN159       0.011976  \n",
       "HN160       0.162963  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_df = pd.read_excel('../preprocessed_data/HN_patient_specific/percent_patient_tpm_cluster.xlsx', index_col=[0, 1]).reset_index()\n",
    "freq_df = freq_df.pivot(index='patient_id', columns='cluster', values='percent').fillna(0) / 100\n",
    "\n",
    "patient_list = freq_df.index\n",
    "\n",
    "freq_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### List all pairs of patient and drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.310107Z",
     "start_time": "2020-11-17T13:25:27.273168Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "pred_delta_df = pd.DataFrame(cadrres_cluster_df.values - drug_info_df[ref_type].values, columns=drug_list, index=cluster_list)\n",
    "pred_cv_df = 100 / (1 + (np.power(2, -pred_delta_df)))\n",
    "pred_kill_df = 100 - pred_cv_df\n",
    "\n",
    "rows = []\n",
    "for p in patient_list:\n",
    "    c_list = freq_df.loc[p][freq_df.loc[p] >= freq_cutoff].index.values\n",
    "    freqs = freq_df.loc[p][freq_df.loc[p] >= freq_cutoff].values\n",
    "\n",
    "    ##### freq sum to 1 (not in use) #####\n",
    "    # freqs = freqs / np.sum(freqs)\n",
    "\n",
    "    p_pred_delta_weighted = np.matmul(pred_delta_df.loc[c_list].values.T, freqs)\n",
    "    p_pred_delta_mat = pred_delta_df.loc[c_list].values\n",
    "    \n",
    "    p_pred_kill_weighted = np.matmul(pred_kill_df.loc[c_list].values.T, freqs)\n",
    "    p_pred_kill_mat = pred_kill_df.loc[c_list].values\n",
    "\n",
    "    for d_i, d_id in enumerate(drug_list):\n",
    "        rows += [[p, d_id] + ['|'.join(c_list)] + ['|'.join([\"{:.14}\".format(f) for f in freqs])] + \n",
    "                 ['|'.join([\"{:.14}\".format(f) for f in p_pred_delta_mat[:, d_i]])] + \n",
    "                 [\"{:.14}\".format(p_pred_delta_weighted[d_i])] +\n",
    "                 ['|'.join([\"{:.14}\".format(f) for f in p_pred_kill_mat[:, d_i]])] + \n",
    "                 [\"{:.14}\".format(p_pred_kill_weighted[d_i])]\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.327414Z",
     "start_time": "2020-11-17T13:25:27.313548Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_delta</th>\n",
       "      <th>delta</th>\n",
       "      <th>cluster_kill</th>\n",
       "      <th>kill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>0.056986626834624|-0.5521462503121|0.532688663...</td>\n",
       "      <td>0.18719816837933</td>\n",
       "      <td>49.012625383709|59.452863251762|40.87266962319...</td>\n",
       "      <td>44.327072791571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-2.8334341716821|-2.6720860912117|-3.778959838...</td>\n",
       "      <td>-3.0281039557461</td>\n",
       "      <td>87.69637799287|86.437921582584|93.209818273866...</td>\n",
       "      <td>85.2974894621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-4.0213711434516|-3.6385077224033|-3.038971912...</td>\n",
       "      <td>-3.3197975989113</td>\n",
       "      <td>94.199124702299|92.56712528151|89.152897129961...</td>\n",
       "      <td>87.040929163578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>1.8712695534725|1.0992860185533|3.087297545393...</td>\n",
       "      <td>2.1769243350696</td>\n",
       "      <td>21.465935078167|31.821936069363|10.52739493194...</td>\n",
       "      <td>17.366424347237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-1.690692592809|-1.3450796341807|-0.8269725475...</td>\n",
       "      <td>-1.173358676562</td>\n",
       "      <td>76.348806194752|71.754940580896|63.95043861070...</td>\n",
       "      <td>66.444651184358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient drug_id      cluster  \\\n",
       "0   HN120    1007  D1|D2|G1|G2   \n",
       "1   HN120     133  D1|D2|G1|G2   \n",
       "2   HN120     201  D1|D2|G1|G2   \n",
       "3   HN120    1010  D1|D2|G1|G2   \n",
       "4   HN120     182  D1|D2|G1|G2   \n",
       "\n",
       "                                           cluster_p  \\\n",
       "0  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "1  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "3  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "4  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "\n",
       "                                       cluster_delta             delta  \\\n",
       "0  0.056986626834624|-0.5521462503121|0.532688663...  0.18719816837933   \n",
       "1  -2.8334341716821|-2.6720860912117|-3.778959838...  -3.0281039557461   \n",
       "2  -4.0213711434516|-3.6385077224033|-3.038971912...  -3.3197975989113   \n",
       "3  1.8712695534725|1.0992860185533|3.087297545393...   2.1769243350696   \n",
       "4  -1.690692592809|-1.3450796341807|-0.8269725475...   -1.173358676562   \n",
       "\n",
       "                                        cluster_kill             kill  \n",
       "0  49.012625383709|59.452863251762|40.87266962319...  44.327072791571  \n",
       "1  87.69637799287|86.437921582584|93.209818273866...    85.2974894621  \n",
       "2  94.199124702299|92.56712528151|89.152897129961...  87.040929163578  \n",
       "3  21.465935078167|31.821936069363|10.52739493194...  17.366424347237  \n",
       "4  76.348806194752|71.754940580896|63.95043861070...  66.444651184358  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_drug_pred_df = pd.DataFrame(rows, columns=['patient', 'drug_id', 'cluster', 'cluster_p', 'cluster_delta', 'delta', 'cluster_kill', 'kill'])\n",
    "single_drug_pred_df = single_drug_pred_df[['patient', 'drug_id', 'cluster', 'cluster_p', 'cluster_delta', 'delta', 'cluster_kill', 'kill']]\n",
    "single_drug_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.338501Z",
     "start_time": "2020-11-17T13:25:27.329523Z"
    }
   },
   "outputs": [],
   "source": [
    "single_drug_pred_df.loc[:, 'drug_name'] = [drug_id_name_dict[d] for d in single_drug_pred_df['drug_id'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.902159Z",
     "start_time": "2020-11-17T13:25:27.340075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dfe27df90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_drug_id_list = ['1007', '133', '201', '1010', '182', '301', '302', '1012']\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103', 'Vorinostat']\n",
    "\n",
    "temp_df = (freq_df.loc[freq_df.index != 'HN182'] >= freq_cutoff).sum()\n",
    "selected_clusters = temp_df.index[temp_df > 0]\n",
    "\n",
    "cmap = plt.cm.get_cmap('bone_r', 10)\n",
    "selected_kill_df = pred_kill_df.loc[selected_clusters, single_drug_id_list]\n",
    "selected_kill_df.columns = single_drug_list\n",
    "selected_kill_df = selected_kill_df[selected_kill_df.sum().sort_values(ascending=False).index]\n",
    "\n",
    "sns.heatmap(selected_kill_df.T, cmap=cmap, vmin=0, vmax=100, linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.920790Z",
     "start_time": "2020-11-17T13:25:27.903657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>log2_median_ic50_hn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Doxorubicin</th>\n",
       "      <td>133</td>\n",
       "      <td>-2.812230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <td>201</td>\n",
       "      <td>-8.520806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PI-103</th>\n",
       "      <td>302</td>\n",
       "      <td>3.056464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vorinostat</th>\n",
       "      <td>1012</td>\n",
       "      <td>0.711127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHA-793887</th>\n",
       "      <td>301</td>\n",
       "      <td>3.606539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obatoclax Mesylate</th>\n",
       "      <td>182</td>\n",
       "      <td>-2.958665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel</th>\n",
       "      <td>1007</td>\n",
       "      <td>-9.792998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib</th>\n",
       "      <td>1010</td>\n",
       "      <td>-1.374969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index  log2_median_ic50_hn\n",
       "Doxorubicin          133            -2.812230\n",
       "Epothilone B         201            -8.520806\n",
       "PI-103               302             3.056464\n",
       "Vorinostat          1012             0.711127\n",
       "PHA-793887           301             3.606539\n",
       "Obatoclax Mesylate   182            -2.958665\n",
       "Docetaxel           1007            -9.792998\n",
       "Gefitinib           1010            -1.374969"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log2_median_ic50_df = drug_info_df.loc[single_drug_id_list][['Drug Name', ref_type]].reset_index().set_index('Drug Name')\n",
    "log2_median_ic50_df.loc[selected_kill_df.sum().sort_values(ascending=False).index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.932145Z",
     "start_time": "2020-11-17T13:25:27.923799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log2_median_ic50_hn\n",
      "0.142375\n",
      "0.002723\n",
      "8.319312\n",
      "1.637083\n",
      "12.180817\n",
      "0.128633\n",
      "0.001127\n",
      "0.385561\n"
     ]
    }
   ],
   "source": [
    "print (ref_type)\n",
    "log2_median_ic50s = log2_median_ic50_df.loc[selected_kill_df.sum().sort_values(ascending=False).index][ref_type].values\n",
    "median_ic50s = np.power(2, log2_median_ic50s)\n",
    "\n",
    "for x in median_ic50s:\n",
    "    print ('{:f}'.format(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.941820Z",
     "start_time": "2020-11-17T13:25:27.934095Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_df = freq_df.loc[freq_df.index != 'HN182'].stack().reset_index()\n",
    "temp_df = temp_df[temp_df[0] >= freq_cutoff]\n",
    "patient_cluster_dict = dict(zip(temp_df['cluster'], temp_df['patient_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.951086Z",
     "start_time": "2020-11-17T13:25:27.944046Z"
    }
   },
   "outputs": [],
   "source": [
    "patient_list = sorted(list(set(temp_df['patient_id'])))\n",
    "cluster_list = temp_df['cluster'].values\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "patient_color_dict = dict(zip(patient_list, [cmap(c) for c in range(len(patient_list))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.959668Z",
     "start_time": "2020-11-17T13:25:27.952789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HN120', 'HN137', 'HN148', 'HN159', 'HN160']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:25:27.965627Z",
     "start_time": "2020-11-17T13:25:27.962298Z"
    }
   },
   "outputs": [],
   "source": [
    "col_colors = [patient_color_dict[patient_cluster_dict[c]] for c in cluster_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-11-17T13:25:26.873Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = plt.cm.get_cmap('bone_r', 11)\n",
    "cg = sns.clustermap(selected_kill_df.T[cluster_list], cmap=cmap, vmin=-10, vmax=100, linewidth=0.5, row_cluster=False, col_cluster=False, col_colors=col_colors, figsize=(8,5.5))\n",
    "\n",
    "_ = cg.ax_heatmap.set_xlabel('Cluster ID')\n",
    "plt.tight_layout()\n",
    "# _ = cg.cax.set_visible(False)\n",
    "\n",
    "cg.savefig('../figure/Fig4A_heatmap_cluster_pred.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-11-17T13:25:26.874Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    single_drug_pred_df.to_csv(out_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(ref_type, model_name), index=False)\n",
    "else:\n",
    "    single_drug_pred_df.to_csv(out_dir + 'pred_drug_kill_{}_{}.csv'.format(ref_type, model_name), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "175.531px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
